A Sensor Invariant Atmospheric Correction Method for Satellite Images

2018 ◽  
Author(s):  
Feng Yin ◽  
J Gomez-Dans ◽  
P Lewis
Keyword(s):  
Satellite Images ◽  
Atmospheric Correction ◽  
Correction Method

scholarly journals Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices

2010 ◽  
Vol 10 (1) ◽  
pp. 89-95 ◽  
Author(s):  
D. G. Hadjimitsis ◽  
G. Papadavid ◽  
A. Agapiou ◽  
K. Themistocleous ◽  
M. G. Hadjimitsis ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Remotely Sensed ◽  
Correction Method ◽  
Atmospheric Effects ◽  
Surface Reflectance ◽  
Remotely Sensed Data ◽  
Agricultural Applications ◽  
Multi Temporal

Abstract. Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydro-meteorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is made to determine evapotranspiration and to examine its dependence on the consideration of atmospheric effects when multi-temporal image data are used. It was found that, without applying any atmospheric correction, the real daily evapotranspiration was less than the one found after applying the darkest pixel atmospheric correction method.

scholarly journals Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

2021 ◽  
Vol 13 (10) ◽  
pp. 1927
Author(s):  
Fuqin Li ◽  
David Jupp ◽  
Thomas Schroeder ◽  
Stephen Sagar ◽  
Joshua Sixsmith ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Correction Method ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Simple Method ◽  
North East ◽  
The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

scholarly journals COMPARING ATMOSPHERIC CORRECTION METHODS FOR LANDSAT OLI DATA

2017 ◽  
Vol 13 (2) ◽  
pp. 105 ◽  
Author(s):  
Esthi Kurnia Dewi ◽  
Bambang Trisakti
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Time Consistency ◽  
The United States ◽  
Correction Method ◽  
Visual Observation ◽  
United States Geological Survey ◽  
Spectral Pattern ◽  
Average Value ◽  
Spectral Patterns

Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.

scholarly journals Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band

2019 ◽  
Vol 11 (3) ◽  
pp. 355 ◽  
Author(s):  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
Liangyun Liu
Keyword(s):  
Chlorophyll Fluorescence ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Correction Method ◽  
Oxygen Absorption ◽  
Atmospheric Transmission ◽  
Absorption Bands ◽  
Transfer Path ◽  
The Difference ◽  
Atmospheric Transmittance

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.

scholarly journals Inversion and Analysis of Land Surface Temperature based on Landsat - a case study of BeiBei District in Chongqing

2020 ◽  
Vol 165 ◽  
pp. 03006
Author(s):  
Zhou Yang ◽  
Liu Na-na
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Exchange Process ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Temperature Inversion ◽  
Correction Method ◽  
Inversion Method ◽  
Temperature Area

Land surface temperature is the surface of the earth’s energy change and the exchange process, which is an important index for a lot of scientific research. In this paper, the surface temperature changes of BeiBei district in Chongqing in the past 20 years were inverted in 6 time phases. The surface temperature inversion method of Landsat remote sensing data was studied, and the atmospheric correction method was adopted to conduct the inversion by using Landsat5TM and landsat8OLI-TIRS image data. The results showed that from 2004 to 2014, the area of high temperature area increased year by year, and the area of low temperature area also increased year by year.

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